With Mosaic Augment

## Overview

With Mosaic Augment is a dataset for instance segmentation tasks - it contains Objects IAHj annotations for 1,680 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Is Augment

## Overview

Is Augment is a dataset for instance segmentation tasks - it contains Objects annotations for 1,352 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Accurate identification of small tea buds is a key technology for tea harvesting robots, which directly affects tea quality and yield. However, due to the complexity of the tea plantation environment and the diversity of tea buds, accurate identification remains an enormous challenge. Current methods based on traditional image processing and machine learning fail to effectively extract subtle features and morphology of small tea buds, resulting in low accuracy and robustness. To achieve accurate identification, this paper proposes a small object detection algorithm called STF-YOLO (Small Target Detection with Swin Transformer and Focused YOLO), which integrates the Swin Transformer module and the YOLOv8 network to improve the detection ability of small objects. The Swin Transformer module extracts visual features based on a self-attention mechanism, which captures global and local context information of small objects to enhance feature representation. The YOLOv8 network is an object detector based on deep convolutional neural networks, offering high speed and precision. Based on the YOLOv8 network, modules including Focus and Depthwise Convolution are introduced to reduce computation and parameters, increase receptive field and feature channels, and improve feature fusion and transmission. Additionally, the Wise Intersection over Union loss is utilized to optimize the network. Experiments conducted on a self-created dataset of tea buds demonstrate that the STF-YOLO model achieves outstanding results, with an accuracy of 91.5% and a mean Average Precision of 89.4%. These results are significantly better than other detectors. Results show that, compared to mainstream algorithms (YOLOv8, YOLOv7, YOLOv5, and YOLOx), the model improves accuracy and F1 score by 5-20.22 percentage points and 0.03-0.13, respectively, proving its effectiveness in enhancing small object detection performance. This research provides technical means for the accurate identification of small tea buds in complex environments and offers insights into small object detection. Future research can further optimize model structures and parameters for more scenarios and tasks, as well as explore data augmentation and model fusion methods to improve generalization ability and robustness.

Augmentations

## Overview

Augmentations is a dataset for instance segmentation tasks - it contains Defects annotations for 3,600 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset consists of 927 image files that were labeled using Roboflow. The dataset is in YOLOv8 format. The dataset is divided into train, validation and test. Data replication processes were also applied.

Preprocessing

• Auto-Orient: Applied
• Resize: Stretch to 640x640

Augmentations

• Outputs per training example: 3
• 90° Rotate: Clockwise, Counter-Clockwise
• Saturation: Between -20% and +20%
• Blur: Up to 1.5px

UAV-Captured Sorghum Crop Line Detection Dataset

Description

This dataset contains UAV-captured images of sorghum fields, annotated for crop line detection. It has been curated to facilitate machine learning research, particularly for developing and evaluating algorithms for agricultural monitoring and analysis.

The dataset has been divided into six separate folders, each formatted for compatibility with different object detection architectures:

• 416x416_augmented: Prepared for use with Detectron2 architectures, such as RetinaNet and Faster R-CNN, with images augmented and resized to 416x416 pixels.
• sorghumfield.v3-416x416_augmented.mt-yolov6: Contains images augmented and tailored for use with the YOLOv6 Meituan architecture.
• sorghumfield.v3-416x416_augmented.yolov5pytorch: Formatted specifically for the YOLOv5 architecture implemented in PyTorch.
• sorghumfield.v3-416x416_augmented.yolov8: Adapted for the latest YOLOv8 architecture, with the same augmentation and resizing.
• sorghumfield.v3i.darknet: Designed for use with YOLOv3, YOLOv4 and YOLOv7 architectures within the Darknet framework.
• sorghumfield.v9i.yolov8_synthetic: An updated set that incorporates synthetic images generated to augment the YOLOv8 dataset.

Each folder contains images that have been manually annotated with bounding boxes to identify crop lines. Annotations were performed using LabelBox, and the data has been segregated into training, validation, and testing sets.

Data Augmentation and Synthetic Data

Data augmentation techniques such as rotations, translations, scaling, and flipping have been applied to increase the diversity and robustness of the dataset. Additionally, synthetic data has been generated and included to enhance the dataset further, providing additional variability and complexity for more effective training of object detection models.

Intended Use

This dataset is intended for use by researchers and practitioners in the fields of computer vision and agriculture technology. It is particularly useful for those developing object detection models for agricultural applications.

Acknowledgments

When utilizing this dataset, please reference the original source of the sorghum images made available by Purdue University and the manual annotations provided in this work.

Citation

If you use this dataset in your research, please cite the following: - Fernandes, G., & Pedro, J. (2023). "Aplicabilidade de Técnicas de Inteligência Artificial na Análise Automática de Imagens Agrícolas Aéreas". Undergraduate Thesis, UnB. - J. Ribera, F. He, Y. Chen, A. F. Habib, and E. J. Delp, "Estimating Phenotypic Traits From UAV Based RGB Imagery", ACM SIGKDD Conference on Knowledge Discovery and Data Mining Workshop - August 2016, San Francisco, CA - J. Ribera, D. Güera, E. J. Delp, "Locating Objects Without Bounding Boxes", Computer Vision and Pattern Recognition (CVPR), June 2019, Long Beach, CA. arXiv:1806.07564.

License

The dataset is available for non-commercial research and educational purposes. For any other use, please contact the authors for permission.

Fine_tune Augment

## Overview

Fine_tune Augment is a dataset for instance segmentation tasks - it contains Cars annotations for 560 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

A specialized acne dataset with 927 annotated images prepared in YOLOv8 format, structured into training, validation, and testing sets with preprocessing and augmentations applied for robust model training.

Train Augmented

## Overview

Train Augmented is a dataset for instance segmentation tasks - it contains Augmented annotations for 12,357 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Overview

This dataset is derived from the [Cell Counting v5 dataset on Roboflow] (https://universe.roboflow.com/cell-counting-hapu2/cell-counting-so7h7 ).
The original dataset was provided in YOLOv8 object detection format.
We created binary masks suitable for UNet-based semantic segmentation tasks.

Additionally, we generated augmented images to increase dataset variability.

Dataset Composition

• Train/Valid/Test Splits
  Each split contains:

  • images/: Source images
  • labels/: YOLO annotation files (kept for reference)
  • masks_binary/: Binary masks for semantic segmentation

• Augmented Images

  • Directory: aug_inference_only/images/
  • Contains 105 augmented images generated from the original 35 images
  • No masks or labels are provided for these augmentations
  • Intended for inference/visualization only (not for training or evaluation)

Data Augmentation

Each of the 35 original images was augmented with 3 additional variations, resulting in 105 augmented images.

Augmentation methods include:
- Random rotation (−90° to 90°)
- Flipping (horizontal, vertical, both)
- Shifting and scaling
- Brightness/contrast adjustment
- Gaussian noise injection

Source

• Base dataset: Roboflow Cell Counting Dataset

License

CC BY 4.0 – This dataset can be shared and adapted with appropriate attribution.

FENCING SCOREBOARD DATASET (YOLOv8 FORMAT)

Project: CMU Fencing Classification Project Author: Michael Stefanov (Carnegie Mellon University) License: MIT Date: 2025

  Description:

Labeled images of fencing scoreboards in lit and unlit states, used to train the YOLOv8 detection model. Includes augmented samples and negatives for robust learning.

  Dataset Summary:

Total Images: ~2000 Splits: train (1600), valid (400) Classes: 1 ("scoreboard") Format: YOLOv8… See the full description on the dataset page: https://huggingface.co/datasets/mastefan/fencing-scoreboard-yolov8.

Presenting my own created and annotated glasses dataset for object detection tasks!

--v2

Introducing the enhanced version of my glasses dataset, curated and refined for object detection enthusiasts!

This v2 dataset boasts 1546 meticulously captured and augmented images, bolstered from an initial set of 770 photos. Every image has undergone two transformative augmentations: - A cyclical application of one of three techniques: RGBShift, HueSaturationValue, or ToGray. - A 90-degree rotation paired with a GaussianBlur application.

By doing this, the dataset not only offers variety but also challenges models to be more resilient and accurate in diverse scenarios. Optimization is key: All images have been resized to a 640x640 resolution, a dimension that has proven optimal for the YOLO model. This ensures models trained on this dataset benefit from improved accuracy, speedier detection, and overall better performance. In addition, the size of the dataset weighs 10 times less than the first version, making it more storage-efficient without compromising on quality. The dataset is strategically divided into an 80% training (1237 images) and 20% validation (309 images) split, ensuring a balanced and robust training regime. Further refining its capabilities, the dataset continues to feature null images that lack glasses. These images are crucial in heightening your model's discernment, enabling it to distinguish glasses from other day-to-day items more effectively. Embark on a transformative computer vision journey with this high-caliber dataset and unlock unparalleled glasses detection competencies.

Happy detecting!

--v1

With 470 meticulously captured images, split into an 85% training and 15% validation set, my dataset empowers you to train your models effectively. I've also included 91 null images, devoid of glasses, to enhance your model's robustness in distinguishing glasses from other household items. Elevate your computer vision projects with my high-quality dataset and accurate glasses detection capabilities.

BRAGAN is a new dataset of Brazilian wildlife developed for object detection tasks, combining real images with synthetic samples generated by Generative Adversarial Networks (GANs). It focuses on five medium and large-sized mammal species frequently involved in roadkill incidents on Brazilian highways: lowland tapir (Tapirus terrestris), jaguarundi (Herpailurus yagouaroundi), maned wolf (Chrysocyon brachyurus), puma (Puma concolor), and giant anteater (Myrmecophaga tridactyla). Its primary goal is to provide a standardized and expanded resource for biodiversity conservation research, wildlife monitoring technologies, and computer vision applications, with an emphasis on automated wildlife detection.

The dataset builds upon the original BRA-Dataset by Ferrante et al. (2022), which was constructed from structured internet searches and manually curated with bounding box annotations. However, while the BRA-Dataset faced limitations in size and variability, BRAGAN introduces a new stage of dataset expansion through GAN-based synthetic image generation, substantially improving both the quantity and diversity of samples. In its final version, BRAGAN comprises approximately 9,238 images, divided into three main groups:

Real images — original photographs from the BRA-Dataset. Total: 1,823.

Classically augmented images — transformations applied to real samples, including rotations (RT), horizontal flips (HF), vertical flips (VF), and horizontal (HS) and vertical shifts (VS). Total: 7,300.

GAN-generated images — synthetic samples created using WGAN-GP models trained separately for each species on preprocessed subsets of the original data. All generated images underwent visual inspection to ensure morphological fidelity and proper framing before inclusion. Total: 115.

The dataset follows an organized directory structure with images/ and labels/ folders, each divided into train/ and val/ subsets, following an 80–20 split. Images are provided in .jpg format, while annotations follow the YOLO standard in .txt files (class_id x_center y_center width height, with normalized coordinates). The file naming convention explicitly encodes the species and the augmentation type for reproducibility.

Designed to be compatible with multiple object detection architectures, BRAGAN has been evaluated on YOLOv5, YOLOv8, and YOLOv11 (variants n, s, and m), enabling the assessment of dataset expansion across different computational settings and performance requirements.

By combining real data, classical augmentations, and high-quality synthetic samples, the BRAGAN provides a valuable resource for wildlife detection, environmental monitoring, and conservation research, especially in contexts where image availability for rare or threatened species is limited.

Coco Augmented Seg 2

## Overview

Coco Augmented Seg 2 is a dataset for instance segmentation tasks - it contains Parking Space T5uH annotations for 769 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

About the Dataset

Overview

This dataset is specifically designed for PCB defect detection using an improved YOLOv8 model. It consists of two sub-datasets: PKU-Market-PCB (Data enhanced version) and DeepPCB.

PKU-Market-PCB (Data Enhanced Version)

Origin

This dataset is derived from the publicly available PCB dataset PKU-Market-PCB, released by Peking University, and has been augmented using data enhancement techniques.

Dataset Details

• Number of images: 3,505
• Training set: 2,773
• Validation set: 366
• Test set: 366

Categories

1. Missing_hole
2. Mouse_bite
3. Open_circuit
4. Short
5. Spur
6. Spurious_copper

DeepPCB

Origin

This dataset is a publicly available PCB defect dataset.

Dataset Details

• Number of images: 1,500
• Training set: 1,200
• Validation set: 150
• Test set: 150

Categories

1. Open
2. Short
3. Mousebite
4. Spur
5. Copper
6. Pin-hole

Illegal-Tools Dataset

Overview

The "illegal-tools" dataset is designed for training object detection models, particularly YOLOv8, to detect illegal objects in online exams. The dataset includes images of various items that are typically prohibited during exams, such as books, earphones, mobile phones, caps, headsets, smartwatches, and sunglasses. This dataset can help in developing automated proctoring systems to ensure exam integrity.

Dataset Split

Total Images: 17,501 Training Set: 92% (16,118 images) Validation Set: 6% (969 images) Test Set: 2% (414 images) The dataset is split to ensure a robust training process, with adequate validation and test sets to evaluate model performance.

Classes

The dataset includes the following classes of illegal objects: 1. Book 2. Earphone 3. Mobile_phone 4. Cap 5. Headset 6. Smart_watch 7. Sunglasses

Preprocessing

Auto-Orient

All images have been auto-oriented to correct for any camera orientation metadata, ensuring that objects are upright and correctly aligned.

Resize

Images have been resized to a fixed dimension of 640x640 pixels using a stretch method. This ensures uniformity across the dataset, making it suitable for models expecting fixed-size input images.

Augmentations

To enhance the robustness of models trained on this dataset, several augmentations have been applied. Each training example has been augmented twice, producing two additional variations of the original image.

Types of Augmentations Horizontal Flip: Random horizontal flipping of images. Blur: Gaussian blur applied up to 1.5 pixels, introducing slight variations in focus. Noise: Random noise affecting up to 1.92% of pixels, simulating real-world image artifacts. These augmentations help in improving the generalization capabilities of models by exposing them to varied visual conditions.

Usage

The dataset is structured in a way that is convenient for training object detection models. Each image is accompanied by corresponding annotation files in the format required by common object detection frameworks.

Vehicle Detection Image Dataset

Introduction

Welcome to the Vehicle Detection Image Dataset! This dataset is meticulously curated for object detection and tracking tasks, with a specific focus on vehicle detection. It serves as a valuable resource for researchers, developers, and enthusiasts seeking to advance the capabilities of computer vision systems.

Objective

The primary aim of this dataset is to facilitate precise object detection tasks, particularly in identifying and tracking vehicles within images. Whether you are engaged in academic research, developing commercial applications, or exploring the frontiers of computer vision, this dataset provides a solid foundation for your projects.

Preprocessing and Augmentation

Both versions of the dataset undergo essential preprocessing steps, including resizing and orientation adjustments. Additionally, the Apply_Grayscale version undergoes augmentation to introduce grayscale variations, thereby enriching the dataset and improving model robustness.

1. Apply_Grayscale

• This version comprises grayscale images and is further augmented to enhance the diversity of training data.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14850461%2F4f23bd8094c892d1b6986c767b42baf4%2Fv2.png?generation=1712264632232641&alt=media" alt="">

2. No_Apply_Grayscale

• This version includes images without applying grayscale augmentation.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14850461%2Fbfb10eb2a4db31a62eb4615da824c387%2Fdetails_v1.png?generation=1712264660626280&alt=media" alt="">

Data Formats

To ensure compatibility with a wide range of object detection frameworks and tools, each version of the dataset is available in multiple formats:

1. COCO
2. YOLOv8
3. YOLOv9
4. TensorFlow

These formats facilitate seamless integration into various machine learning frameworks and libraries, empowering users to leverage their preferred development environments.

Real-Time Object Detection

In addition to image datasets, we also provide a video for real-time object detection evaluation. This video allows users to test the performance of their models in real-world scenarios, providing invaluable insights into the effectiveness of their detection algorithms.

Getting Started

To begin exploring the Vehicle Detection Image Dataset, simply download the version and format that best suits your project requirements. Whether you are an experienced practitioner or just embarking on your journey in computer vision, this dataset offers a valuable resource for advancing your understanding and capabilities in object detection and tracking tasks.

Citation

If you utilize this dataset in your work, we kindly request that you cite the following:

Parisa Karimi Darabi. (2024). Vehicle Detection Image Dataset: Suitable for Object Detection and tracking Tasks. Retrieved from https://www.kaggle.com/datasets/pkdarabi/vehicle-detection-image-dataset/

Feedback and Contributions

I welcome feedback and contributions from the Kaggle community to continually enhance the quality and usability of this dataset. Please feel free to reach out if you have suggestions, questions, or additional data and annotations to contribute. Together, we can drive innovation and progress in computer vision.

Blood Stains Augmented

## Overview

Blood Stains Augmented is a dataset for instance segmentation tasks - it contains Bloodstains annotations for 856 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Contains knee region x-rays collected from various hospital in northern part of India. Each images is of 640 x 640 pixels and they are with their knee region bounding-box YOLOv8 annotations files. split into TRAIN, VALIDATE and TEST in ratio 0.7: 0.2: 0.1

--more info - Augmentation: - Flip: Horizontal, Vertical - Rotation: Between -15° and +15° - Total images: original + Augmented = Total images 970 + 1322 = 2292

• KEERTHIVASAN(pre-processing)
• PRAVEEN(annotations) annotations made by using label-studio and roboflow

Space Debris Detection Dataset

Overview

This dataset is designed for the detection and classification of space debris, aiming to enhance space situational awareness and contribute to the mitigation of space debris hazards. It provides annotated images suitable for training machine learning models in object detection tasks.

Dataset Details

• Total Images: 1,265
• Classes: 11 ('cheops', 'debris', 'double_start', 'earth_observation_sat_1', 'lisa_pathfinder', 'proba_2', 'proba_3_csc', 'proba_3_ocs', 'smart_1', 'soho', 'xmm_newton')
• Annotations: Each image is annotated with bounding boxes corresponding to the debris and satellite classes.

Data Splits

• Training Set: 88% (1,107 images)
• Validation Set: 8% (106 images)
• Test Set: 4% (52 images)

Preprocessing and Augmentation

• Preprocessing:

  • Auto-orientation applied to ensure consistent image orientation.
  • Images resized to 640x640 pixels using stretch resizing.

• Augmentations:

  • Horizontal flips
  • 90° rotations (clockwise and counter-clockwise)
  • Addition of noise affecting up to 1.09% of pixels
  • Each training example generates 3 augmented versions to enhance model robustness.

Applications

This dataset is suitable for developing and evaluating object detection models focused on identifying space debris and satellites. Potential applications include:

• Space Situational Awareness: Enhancing the tracking and monitoring of space objects to prevent collisions.
• Autonomous Navigation: Assisting spacecraft in detecting and avoiding debris.
• Research and Development: Serving as a benchmark for testing new algorithms in object detection and space debris identification.

Citation

If you utilize this dataset in your research or projects, please cite it as follows:

@misc{space-debris-and-satellite-dataset,
 title = {Space Debris and Satellite Dataset},
 type = {Open Source Dataset},
 author = {Mahmoud},
 howpublished = {\url{https://universe.roboflow.com/mahmoud-xm4kv/space-debris-and-satilite}},
 url = {https://universe.roboflow.com/mahmoud-xm4kv/space-debris-and-satilite},
 journal = {Roboflow Universe},
 publisher = {Roboflow},
 year = {2024},
 month = {sep},
 note = {visited on 2024-10-04}
}